This article presents a comprehensive overview of methods and techniques used for the evaluation of user-adaptive systems. It describes the methodologies derived both from the evaluation of human–computer interaction systems and from information retrieval and information filtering systems by giving examples of the application of these methodologies in the user-adaptive systems. The state of the art and the main results in the evaluation of these systems are reported. In particular, empirical evaluation and layered approaches are discussed in detail. Finally, focus on less explored methodologies, such as qualitative approaches (e.g. Grounded Theory), is proposed.

People in human-computer interaction have learned a great deal about how to persuade and influence users of computing technology. They have much less well-founded knowledge about how to help users choose for themselves. It’s time to correct this imbalance. A first step is to organize the vast amount of relevant knowledge that has been built up in psychology and related fields in terms of two comprehensive but easy-to-remember models: The ASPECT model answers the question “How do people make choices?” by describing six choice patterns that choosers apply alternately or in combination, based on Attributes, Social influence, Policies, Experience, Consequences, and Trial and error. The ARCADE model answers the question “How can we help people make better choices?” by describing six general high-level strategies for supporting choice: Access information and experience, Represent the choice situation, Combine and compute, Advise about processing, Design the domain, and Evaluate on behalf of the chooser. These strategies can be implemented with straightforward interaction design, but for each one there are also specifically relevant technologies. Combining these two models, we can understand virtually all existing and possible approaches to choice support as the application of one or more of the ARCADE strategies to one or more of the ASPECT choice patterns. After introducing the idea of choice architecture for humancomputer interaction and the key ideas of the ASPECT and ARCADE models, we discuss each of the ASPECT patterns in detail and show how the high-level ARCADE strategies can be applied to it to yield specific tactics. We then apply the two models in the domains of online communities and privacy. Most of our examples concern choices about the use of computing technology, but the models are equally applicable to everyday choices made with the help of computing technology.

In this survey paper, we focus on smart interactive technologies and providing a picture of the current state of the art, exploring the way new discoveries and recent technologies changed workers’ operations and activities on the factory floor. We focus in particular on the Industry 4.0 and 5.0 visions, wherein smart interactive technologies can bring benefits to the intelligent behavior machines can expose in a human-centric AI perspective. We consider smart technologies wherein the intelligence may be in and/or behind the user interfaces, and for both groups we try to highlight the importance of designing them with a human-centric approach, framed in the smart factory context. We review relevant work in the field with the aim of highlighting the pros and cons of each technology and its adoption in the industry. Furthermore, we try to collect guidelines for the human-centric integration of smart interactive technologies in the smart factory. In this wa y, we hope to provide the future designers and adopters of such technologies with concrete help in choosing among different options and implementing them in a user-centric manner. To this aim, surveyed works have been also classified based on the supported task(s) and production process phases/activities: access to knowledge, logistics, maintenance, planning, production, security, workers’ wellbeing, and warehousing.